Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida
Prediction, identification, understanding and visualization of relationship between factors affecting mortality in ACS patients using feature selection and ML algorithms. Feature selection, classification and pattern recognition methods have been used in this research. From a group of 1480 patients...
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| Format: | Thesis |
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2019
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| Online Access: | http://studentsrepo.um.edu.my/12326/1/Nanyonga.pdf http://studentsrepo.um.edu.my/12326/2/Nanyonga_Aziida.pdf http://studentsrepo.um.edu.my/12326/ |
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| Institution: | Universiti Malaya |
| Summary: | Prediction, identification, understanding and visualization of relationship between factors affecting mortality in ACS patients using feature selection and ML algorithms. Feature selection, classification and pattern recognition methods have been used in this research. From a group of 1480 patients drawn from the Acute Coronary Syndrome Malaysian registry, 302 people satisfied the inclusion criteria, and 54 variables were duly considered. Combinations of feature selection and classification algorithms were used for mortality prediction post ACS. Self-Organizing Feature (SOM) was used to visualize and identify the relationship and pattern between factors affecting mortality after ACS. Prediction models' performance criteria was measured using area under the curve (AUC) ranged from 0.62 to 0.795. The best model (RF) executed using 5 predictors (Age, TG, creatinine, Troponin and TC). Most model’s performance plateaued using five predictors. The best performing model was compared with TIMI using an additional dataset that resulted in the ML model outperforming TIMI score (AUC 0.75 vs 0.60). Machine learning techniques for prediction and visualization of mortality related to ACS is presented in this study. The selected algorithms effectively show increase in prediction performance with decreasing features. Combination of ML prediction and visualization capabilities indicate effectiveness in predicting outcomes for clinical cardiology settings.
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